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Diagnostics
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Machine Learning in Microbiology and Infectious Disease Diagnosis
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Machine Learning in Microbiology and Infectious Disease Diagnosis

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Diagnostic Microbiology and Infectious Disease".

Deadline for manuscript submissions: closed (28 February 2022) | Viewed by 6272

Special Issue Editor

Dr. Hsin-Yao Wang

E-Mail Website
Guest Editor
Department of Laboratory Medicine, Chang Gung Memorial Hospital, Linkou Branch, Taoyuan 333, Taiwan
Interests: laboratory medicine; medical AI; translational medicine
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

A variety of diagnostic tools (e.g., NGS, mass spectrometry, image) and clinical scores have been developed and applied to microbiology or infectious diseases in recent decades. Massive clinicopathological data are generated by these tools. However, the tools or scores are generally developed and validated separately. Comprehensive analysis and interpretation of the trans-omic data is still largely lacking. As an analytical tool with an excellent ability to cope with massive data and pattern recognition, machine learning (ML) algorithms are appropriate methods. For a successful harness of ML in the fields of microbiology and infectious disease diagnosis, there is a plethora of topics that are worthy of investigation. With the aim of better harnessing ML in these fields, the topics of the Special Issue include tailored/standardized techniques of diagnostic tools/scores for ML, optimal pre-processing of analytical data, training/validation of ML algorithms, and issues of real-world implementation. It is my pleasure to invite the submission of high-quality studies relevant to the aforementioned topics. I believe that the investigation and documentation of the key issues will help to deliver considerable impacts in the fields of microbiology and infectious disease diagnosis.

Dr. Hsin-Yao Wang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (2 papers)

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Research

10 pages, 985 KiB  
Article
Deep-Learning-Aided Detection of Mycobacteria in Pathology Specimens Increases the Sensitivity in Early Diagnosis of Pulmonary Tuberculosis Compared with Bacteriology Tests
by Yoshiaki Zaizen, Yuki Kanahori, Sousuke Ishijima, Yuka Kitamura, Han-Seung Yoon, Mutsumi Ozasa, Hiroshi Mukae, Andrey Bychkov, Tomoaki Hoshino and Junya Fukuoka
Diagnostics 2022, 12(3), 709; https://doi.org/10.3390/diagnostics12030709 - 14 Mar 2022
Cited by 12 | Viewed by 3120
Abstract
The histopathological diagnosis of mycobacterial infection may be improved by a comprehensive analysis using artificial intelligence. Two autopsy cases of pulmonary tuberculosis, and forty biopsy cases of undetected acid-fast bacilli (AFB) were used to train AI (convolutional neural network), and construct an AI [...] Read more.
The histopathological diagnosis of mycobacterial infection may be improved by a comprehensive analysis using artificial intelligence. Two autopsy cases of pulmonary tuberculosis, and forty biopsy cases of undetected acid-fast bacilli (AFB) were used to train AI (convolutional neural network), and construct an AI to support AFB detection. Forty-two patients underwent bronchoscopy, and were evaluated using AI-supported pathology to detect AFB. The AI-supported pathology diagnosis was compared with bacteriology diagnosis from bronchial lavage fluid and the final definitive diagnosis of mycobacteriosis. Among the 16 patients with mycobacteriosis, bacteriology was positive in 9 patients (56%). Two patients (13%) were positive for AFB without AI assistance, whereas AI-supported pathology identified eleven positive patients (69%). When limited to tuberculosis, AI-supported pathology had significantly higher sensitivity compared with bacteriology (86% vs. 29%, p = 0.046). Seven patients diagnosed with mycobacteriosis had no consolidation or cavitary shadows in computed tomography; the sensitivity of bacteriology and AI-supported pathology was 29% and 86%, respectively (p = 0.046). The specificity of AI-supported pathology was 100% in this study. AI-supported pathology may be more sensitive than bacteriological tests for detecting AFB in samples collected via bronchoscopy. Full article
(This article belongs to the Special Issue Machine Learning in Microbiology and Infectious Disease Diagnosis)
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13 pages, 1881 KiB  
Article
Investigating Unfavorable Factors That Impede MALDI-TOF-Based AI in Predicting Antibiotic Resistance
by Hsin-Yao Wang, Yu-Hsin Liu, Yi-Ju Tseng, Chia-Ru Chung, Ting-Wei Lin, Jia-Ruei Yu, Yhu-Chering Huang and Jang-Jih Lu
Diagnostics 2022, 12(2), 413; https://doi.org/10.3390/diagnostics12020413 - 5 Feb 2022
Cited by 1 | Viewed by 2082
Abstract
The combination of Matrix-Assisted Laser Desorption/Ionization Time-of-Flight (MALDI-TOF) spectra data and artificial intelligence (AI) has been introduced for rapid prediction on antibiotic susceptibility testing (AST) of Staphylococcus aureus. Based on the AI predictive probability, cases with probabilities between the low and high [...] Read more.
The combination of Matrix-Assisted Laser Desorption/Ionization Time-of-Flight (MALDI-TOF) spectra data and artificial intelligence (AI) has been introduced for rapid prediction on antibiotic susceptibility testing (AST) of Staphylococcus aureus. Based on the AI predictive probability, cases with probabilities between the low and high cut-offs are defined as being in the “grey zone”. We aimed to investigate the underlying reasons of unconfident (grey zone) or wrong predictive AST. In total, 479 S. aureus isolates were collected and analyzed by MALDI-TOF, and AST prediction and standard AST were obtained in a tertiary medical center. The predictions were categorized as correct-prediction group, wrong-prediction group, and grey-zone group. We analyzed the association between the predictive results and the demographic data, spectral data, and strain types. For methicillin-resistant S. aureus (MRSA), a larger cefoxitin zone size was found in the wrong-prediction group. Multilocus sequence typing of the MRSA isolates in the grey-zone group revealed that uncommon strain types comprised 80%. Of the methicillin-susceptible S. aureus (MSSA) isolates in the grey-zone group, the majority (60%) comprised over 10 different strain types. In predicting AST based on MALDI-TOF AI, uncommon strains and high diversity contribute to suboptimal predictive performance. Full article
(This article belongs to the Special Issue Machine Learning in Microbiology and Infectious Disease Diagnosis)
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